ML.NET |為 .NET 打造的機器學習

專爲 .NET 開發人員打造

有了 ML.NET，您可以使用現有的 .NET 技能輕鬆地將 ML 整合到您的 .NET 應用程式中，而且不需要具備任何的機器學習經驗。

使用 AutoML 輕鬆自訂 ML

ML.NET 提供 AutoML 及生產效率工具，協助您輕鬆組建、訓練及部署高品質的自訂 ML 模型。

已使用 TensorFlow & 等擴充

ML.NET 可讓您利用其他熱門的 ML 程式庫，例如 Infer.NET、TensorFlow 和 ONNX，以用於其他 ML 情節。

已信賴且經大規模實證

使用知名的 Microsoft 產品所使用的相同 ML 架構，例如 Power BI、Microsoft Defender、Outlook 和 Bing。

C#
F#

//Step 1. Create an ML Context
var ctx = new MLContext();

//Step 2. Read in the input data from a text file for model training
IDataView trainingData = ctx.Data
    .LoadFromTextFile<ModelInput>(dataPath, hasHeader: true);

//Step 3. Build your data processing and training pipeline
var pipeline = ctx.Transforms.Text
    .FeaturizeText("Features", nameof(SentimentIssue.Text))
    .Append(ctx.BinaryClassification.Trainers
        .LbfgsLogisticRegression("Label", "Features"));

//Step 4. Train your model
ITransformer trainedModel = pipeline.Fit(trainingData);

//Step 5. Make predictions using your trained model
var predictionEngine = ctx.Model
    .CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel);

var sampleStatement = new ModelInput() { Text = "This is a horrible movie" };

var prediction = predictionEngine.Predict(sampleStatement);

//Step 1. Create an ML Context
let ctx = MLContext()

//Step 2. Read in the input data from a text file
let trainingData = ctx.Data.LoadFromTextFile<ModelInput>(dataPath, hasHeader=true)

//Step 3. Build your data processing and training pipeline
let scope = ctx.BinaryClassification.Trainers.LbfgsLogisticRegression("Label", "Features")
let pipeline = ctx.Transforms.Text.FeaturizeText("Features", "Text").Append(scope)

//Step 4. Train your model
let trainedModel = pipeline.Fit(trainingData)

//Step 5. Make predictions using your model
let predictionEngine = ctx.Model.CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel)

let sampleStatement = { Label = false; Text = "This is a horrible movie" }

let prediction = predictionEngine.Predict(sampleStatement)